Building cheap presence sensors with common consumer hardware

We’ve been experimenting with presence sensors which use signals emitted by personal devices, such as smartphones. The sensors we’ve built are surprisingly good value for money.

Understanding where citizens are, how we move and how we use the city’s facilities at different times is key to a smart city. Different approaches can be used to sense the ‘pulse’ of the city – satellite imaging, crowdsourced location data, social network presences, etc. However, many of these approaches require the deployment of complex and costly mechanisms, while representing only a narrow slice of the required information. They also may have complex organisational and legal implications. As mobile phones and personal computing devices are becoming more and more common, each citizen now emits a variety of signals throughout their movement around the city that can be picked up as a way to sense their presence.

MK:Smart is therefore investigating, small, easy to deploy sensors that can detect wifi signals emitted by common personal devices such as smartphones, and is looking into how to use this to infer the amount of activity in the surrounding area and how these activities evolve in time. Each sensor is based on a Raspberry Pi, a cheap, fully functional credit card-size computer, attached to standard wifi receivers to monitor signals in its surroundings. Dedicated software loaded onto the sensors make them data endpoints that, once connected to a network of several devices, provide the required information to locate the presence of people carrying the detected devices.

… when it comes to personal data, the sensors are designed not to expose or store information that can be used to identify or track personal devices.

We started experimenting with the presence sensors on 1 April at an MK:Smart meeting. Four of the devices were deployed to map the location of attendees on the floor plan of the meeting room , showing how the room would fill-up at the beginning of the meeting, and how the patterns of activity change during talks, coffee breaks, etc. Further tests are now being carried out in larger areas where being able to detect and predict patterns in the amount of activity would provide a valuable service (restaurants, shops, etc.).

As well as the potential value in applying this technology, these experiments also represent an opportunity for MK:Smart to research some of the key aspects of data and sensing infrastructures for smart cities. For example, when it comes to personal data, the sensors are designed not to expose or store information that can be used to identify or track personal devices.